CT-GenAI최신시험덤프공부자료 & CT-GenAI최신시험기출문제모음

그 외, Fast2test CT-GenAI 시험 문제집 일부가 지금은 무료입니다: https://drive.google.com/open?id=1lY6PpyZU2zit9S0AkwG2Yyb5JdqFbEfv

Fast2test에서는 ISQI인증 CT-GenAI시험을 도전해보시려는 분들을 위해 퍼펙트한 ISQI인증 CT-GenAI덤프를 가벼운 가격으로 제공해드립니다.덤프는ISQI인증 CT-GenAI시험의 기출문제와 예상문제로 제작된것으로서 시험문제를 거의 100%커버하고 있습니다. Fast2test제품을 한번 믿어주시면 기적을 가져다 드릴것입니다.

ISQI CT-GenAI Exam Syllabus Topics:

SectionWeightObjectives
Testing Activities for Generative AI30%- Requirements-Based Testing
  • 1. Non-functional requirements for AI-based systems
  • 2. Functional requirements for AI-based systems
  • 3. AI-related quality requirements
- Traceability and Documentation
  • 1. Documentation requirements for AI testing
  • 2. Test coverage of AI model components
- Prompt-Based Testing
  • 1. Test case design using prompts
  • 2. Prompt engineering basics
  • 3. Test data creation with GenAI
- Model and Output Evaluation
  • 1. Checkpoint testing
  • 2. Automated evaluation methods
  • 3. Metamorphic testing
  • 4. Human evaluation methods
  • 5. Output correctness and quality assessment
Fundamentals of Generative AI20%- Generative AI Concepts
  • 1. AI model behavior
  • 2. Model types (Base, Instruction-tuned, RAG)
  • 3. Hallucinations
  • 4. Alignment and guardrails
  • 5. Emergent capabilities and limitations
  • 6. Training data and context windows
- AI Development Lifecycle
  • 1. Deployment and monitoring
  • 2. Evaluation
  • 3. Model training and fine-tuning
  • 4. Data collection, preparation, and curation
- AI Terminology
  • 1. Deep Learning
  • 2. Artificial Intelligence (AI)
  • 3. Generative AI (GenAI)
  • 4. Large Language Models (LLMs)
  • 5. Transformer architecture
  • 6. Machine Learning (ML)
  • 7. Tokens and prompts
Tools for Testing Generative AI20%- Using Tools for Common Testing Activities
  • 1. Prompt testing tools
  • 2. Model evaluation tools
  • 3. Simulation and monitoring tools
  • 4. Security testing tools
- Testing Tools Overview
  • 1. Categories of GenAI testing tools
  • 2. Selecting appropriate tools for specific testing needs
Risks and Testing Challenges for Generative AI30%- Testing Challenges for Generative AI
  • 1. Subjectivity of quality assessment
  • 2. Test oracle problem
  • 3. Ethical testing concerns
  • 4. Coverage challenges
  • 5. Complexity of the AI component
  • 6. Non-deterministic output behavior
  • 7. Regulatory and compliance considerations
- Quality Risks Specific to Generative AI
  • 1. Input sensitivity (prompt brittleness)
  • 2. Inappropriate output for the context
  • 3. Incorrect or fabricated outputs (hallucinations)
  • 4. Offensive, harmful, or biased content
  • 5. Dependency on external components
  • 6. Inconsistent responses across runs

>> CT-GenAI최신 시험덤프공부자료 <<

시험대비 CT-GenAI최신 시험덤프공부자료 인증공부

우리사이트가 다른 덤프사이트보다 우수한 점은 바로 자료들이 모두 전면적이고 적중률과 정확입니다. 때문에 우리Fast2test를 선택함으로ISQI인증CT-GenAI시험준비에는 최고의 자료입니다. 여러분이 성공을 위한 최고의 자료입니다.

최신 AI Testing CT-GenAI 무료샘플문제 (Q28-Q33):

질문 # 28
Who typically defines the system prompt in a testing workflow?

정답：D

설명：
In professional Generative AI applications, thesystem prompt(sometimes called the system message) is the foundational set of instructions that defines the AI's persona, boundaries, and overall behavior. In a testing workflow, this is typically defined by atester or test engineerwho is configuring the AI assistant for a specific project. Unlike the user prompt, which changes with every interaction, the system prompt remains relatively static and acts as a "guardrail" to ensure the model stays in its role (e.g., "You are an expert in ISO
26262 automotive testing standards"). By defining the system prompt, the tester ensures that the model consistently uses specific terminology, adheres to data privacy constraints, and formats its output according to the team's requirements. While end users (Option B) provide the task-specific input, they do not usually have the permissions or technical need to alter the underlying system-level instructions. Similarly, while CI servers (Option C) might trigger the prompt, they do not "define" the human-centric logic contained within it.
Properly crafting the system prompt is a core part of setting up an AI-augmented test environment.


질문 # 29
You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?

정답：A

설명：
The most effective way to improve an LLM's performance on complex tasks likeimpact analysisis to provide a detailed, multi-stepInstructionorChain-of-Thoughtstructure. Option D is the best improvement because it breaks the "impact analysis" task into logical sub-tasks: mapping changes to modules, identifying related test cases, and prioritizing them based on risk and complexity. This structured approach guides the LLM through the "reasoning" steps a human expert would take, significantly reducing the likelihood of a superficial or incorrect analysis. While specifying a specialized role (Option B) or adding technical references (Option A) can help set the tone, they do not provide the model with the logical framework required to execute the task accurately. By explicitly defining theprocessthe LLM should follow, the tester ensures that the model evaluates the "depth" of the change rather than just listing files. This results in a more robust and actionable regression test suite, which is the primary goal of impact analysis in a modern software development lifecycle.


질문 # 30
Consider applying the meta-prompting technique to generate automated test scripts for API testing. You need to test a REST API endpoint that processes user registration with validation rules. Which one of the following prompts is BEST suited to this task?

정답：D

설명：
Option A is the superior choice because it strictly adheres to thestructured prompting patternrecommended in the CT-GenAI syllabus. This pattern divides the prompt into six distinct components:Role, Context, Instruction, Input Data, Constraints, and Output Format.By specifying theRole(Senior Test Automation Engineer), the model accesses relevant technical knowledge. TheInstructionis specific about using pytest and the requests library, and it explicitly lists both positive and negative scenarios. Most importantly, the Constraintssection provides the necessary "guardrails" for the code structure, such as the use of fixtures and clear assertions. Options B, C, and D are increasingly vague and fail to provide the model with the necessary technical boundaries to produce "production-ready" testware. Structured prompting reduces the "probabilistic drift" of the model, ensuring the output is not just functional code, but a script that follows industry-standard testing patterns (like modularity and clean naming conventions), making it directly usable within a CI/CD pipeline.


질문 # 31
What are the three key phases in adopting GenAI in a test organization?

정답：B

설명：
According to the strategic frameworks for AI adoption (as detailed in the CT-GenAI and related ISO/IEC
42001 standards), the journey toward organizational AI maturity follows three primary phases. TheDiscovery phase involves identifying potential use cases, assessing current technical readiness, and understanding the legal/risk landscape. TheInitiation and Usage Definitionphase is where the organization sets the "ground rules"-defining which tools are approved, establishing system prompts, creating prompt libraries, and training the staff on prompt engineering. This phase transitions the AI from a novelty into a structured capability. Finally, theUtilization and Iterationphase is the ongoing process where GenAI is used in daily testing activities, and its outputs are constantly monitored, measured, and improved through feedback loops.
This ensures the strategy remains dynamic and adapts to new model capabilities or changing project requirements. Options B, C, and D represent standard project management or IT lifecycles but do not capture the specific "learning and refinement" nature required for successful Generative AI integration in a testing department.


질문 # 32
What does an embedding represent in an LLM?

정답：C

설명：
Embeddingsare a fundamental concept in modern Natural Language Processing (NLP) and LLMs. They are high-dimensional numerical vectors-essentially lists of numbers-that represent the meaning (semantics) of a piece of text (a word, sentence, or document). Unlike traditional keyword matching, which looks for identical strings of characters, embeddings allow the model to understand the "closeness" of concepts. For example, in a vector space, the word "bug" would be mathematically closer to "defect" or "error" than to
"feature" or "requirement." This captures the semantic relationship between terms. This technology is the backbone of Retrieval-Augmented Generation (RAG) used in testing: when a tester queries a documentation set, the system converts the query into an embedding and looks for other chunks of text with similar vector values. This allows the AI to retrieve relevant context even if the exact keywords do not match. It is not about logical rules (Option C) or groups of tokens (Option A), but rather a mathematical representation of language that enables machines to process human meaning.


질문 # 33
......

IT인증시험에 도전해보려는 분들은 회사에 다니는 분들이 대부분입니다. 승진을 위해서나 연봉협상을 위해서나 자격증 취득은 지금시대의 필수입니다. Fast2test의ISQI인증 CT-GenAI덤프는 회사다니느라 바쁜 나날을 보내고 있는 분들을 위해 준비한 시험준비공부자료입니다. Fast2test의ISQI인증 CT-GenAI덤프를 구매하여 pdf버전을 공부하고 소프트웨어버전으로 시험환경을 익혀 시험보는게 두렵지 않게 해드립니다. 문제가 적고 가격이 저렴해 누구나 부담없이 애용 가능합니다. Fast2test의ISQI인증 CT-GenAI덤프를 데려가 주시면 기적을 안겨드릴게요.

CT-GenAI최신 시험 기출문제 모음: https://kr.fast2test.com/CT-GenAI-premium-file.html

2026 Fast2test 최신 CT-GenAI PDF 버전 시험 문제집과 CT-GenAI 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1lY6PpyZU2zit9S0AkwG2Yyb5JdqFbEfv